Abstract
BackgroundTimely monitoring of COVID-19 impact on mortality is critical for rapid risk assessment and public health action.AimBuilding upon well-established models to estimate influenza-related mortality, we propose a new statistical Attributable Mortality Model (AttMOMO), which estimates mortality attributable to one or more pathogens simultaneously (e.g. SARS-CoV-2 and seasonal influenza viruses), while adjusting for seasonality and excess temperatures.MethodsData from Nationwide Danish registers from 2014-week(W)W27 to 2020-W22 were used to exemplify utilities of the model, and to estimate COVID-19 and influenza attributable mortality from 2019-W40 to 2020-W20.ResultsSARS-CoV-2 was registered in Denmark from 2020-W09. Mortality attributable to COVID-19 in Denmark increased steeply, and peaked in 2020-W14. As preventive measures and national lockdown were implemented from 2020-W12, the attributable mortality started declining within a few weeks. Mortality attributable to COVID-19 from 2020-W09 to 2020-W20 was estimated to 16.2 (95% confidence interval (CI): 12.0 to 20.4) per 100,000 person-years. The 2019/20 influenza season was mild with few deaths attributable to influenza, 3.2 (95% CI: 1.1 to 5.4) per 100,000 person-years.ConclusionAttMOMO estimates mortality attributable to several pathogens simultaneously, providing a fuller picture of mortality by COVID-19 during the pandemic in the context of other seasonal diseases and mortality patterns. Using Danish data, we show that the model accurately estimates mortality attributable to COVID-19 and influenza, respectively. We propose using standardised indicators for pathogen circulation in the population, to make estimates comparable between countries and applicable for timely monitoring.
Highlights
The corona virus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has, as up to 31 October 2020, resulted in 45.4 million confirmed infections and 1.2 million deaths globally [1]
We have extended and improved this model into a general Attributable Mortality Monitoring (AttMOMO) model, enabling estimation of attributable mortality from several pathogens simultaneously, e.g. COVID-19 and seasonal influenza
For timely surveillance of mortality due to for example COVID-19, we propose a general model decomposing the total number of deaths into those attributable to one or more infectious pathogens circulating in a population, and those attributable to deaths due to excess temperatures and other seasonal patterns
Summary
The corona virus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has, as up to 31 October 2020, resulted in 45.4 million confirmed infections and 1.2 million deaths globally [1]. To provide effective pandemic risk assessment, preparedness and response, including evaluation of the effect of implemented measures in society, timely monitoring of COVID-19-attributable outcomes is critical. COVID19 fatality is often counted as case fatality, i.e. number of all-cause deaths within a fixed period after a positive SARS-CoV-2 test (normally 30 days). Methods: Data from Nationwide Danish registers from 2014-week(W)W27 to 2020-W22 were used to exemplify utilities of the model, and to estimate COVID-19 and influenza attributable mortality from 2019-W40 to 2020-W20. Conclusion: AttMOMO estimates mortality attributable to several pathogens simultaneously, providing a fuller picture of mortality by COVID-19 during the pandemic in the context of other seasonal diseases and mortality patterns. We propose using standardised indicators for pathogen circulation in the population, to make estimates comparable between countries and applicable for timely monitoring
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
